Abstract
Many different models and services to perform Sentiment Analysis are available. It is often difficult to choose the right one for the use case of interest. This thesis analyses relevant techniques that have been successfully applied to classify sentiment polarity and it proposes a comparison of their performances based on experiments run on the dataset Sentiment140. Moreover, it proposes an analysis to understand when the models agree on the correct classification to highlight the margin of improvement that is possible to achieve in theory. Three main macrocategories of models are considered: traditional models based on mathematical theorems or intuitions (Naive Bayes, Support Vector Machine, Logistic Regression and Random Forest), neural models (ANN, CNN, Bi-LSTM and a hybrid approach) and classification services offered by top technology companies (AWS Comprehend, Google Natural Language API and Meaning Cloud). The tested models achieved very similar performances, with the best model represented by Logistic Regression. Despite the potential of neural models and the advantages of ready-to-use services, traditional models proved to be the best trade-off and provided the best performances. Analysing when the models agree, it was possible to observe that there is a subset of the dataset that is not correctly classified by any model, although in theory it is possible to achieve much better performances than those obtained by individual models.
